In [100]:
%matplotlib inline
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table
from sklearn.neighbors import KernelDensity as KD
from scipy import integrate
from scipy import interpolate
from matplotlib import gridspec
sys.path.insert(0, '/home/john/densityplot/densityplot')
from densityplot.hex_scatter import hex_contour as hex_contour
from scipy import interpolate
from scipy import stats
from sklearn.neighbors import KernelDensity as kde
In [101]:
data = Table.read('/Users/johntimlin/Catalogs/QSO_candidates/Final_S82_candidates_full/QSOs_S82_wzspec_wcolors_wphotoz.fits')
#data = Table.read('/home/john/Catalogs/QSO_Candidates/Final_S82_candidates_full/QSOs_S82_wzspec_wcolors_wphotoz.fits')
zspec = data['ZSPEC']
zphotnw = data['zphotNW']
zphotrf = data['zphotRF']
imag = 22.5-2.5*np.log10(data['iflux'])
print len(imag)
print len(imag[imag<=20.2])
print len(imag[imag>20.2])
In [102]:
#Histogram of the difference between zphot and zspec
dznw = zphotnw-zspec
dzrf = zphotrf-zspec
dznw2 = dznw[(dznw>0) | (dznw <=0)]
dzrf2 = dzrf[(dzrf>0) | (dzrf <=0)]
dznwhz = zphotnw[zspec>=2.9]-zspec[zspec>=2.9]
dznwhz2 = dznwhz[(dznwhz>0) | (dznwhz <=0)]
print len(dznw2[(dznw2>-0.3) & (dznw2<0.3)])/float(len(dznw2))
In [127]:
print len(dznw2[(dznw2>-0.3) & (dznw2<0.3)])/float(len(dznw2))
print len(dznwhz2[(dznwhz2>-0.1) & (dznwhz2<0.1)])/float(len(dznwhz2))
#Plotting Parameters (Replace with Group code call!)
params = {'legend.fontsize': 12, 'xtick.labelsize': 12, 'ytick.labelsize': 12, 'xtick.major.width':2, 'xtick.minor.width':2, 'ytick.major.width':2, 'ytick.minor.width':2, 'xtick.major.size':8, 'xtick.minor.size':4, 'ytick.major.size':8, 'ytick.minor.size':4}
plt.rcParams.update(params)
plt.rc("axes", linewidth=2.0)
#Plot for paper
fig = plt.figure(1,figsize = (10,10))
gs = gridspec.GridSpec(1, 2, height_ratios=[1, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1])
ax0.scatter(zphotnw,zspec, s = 1, edgecolor = 'r',facecolor = 'r', alpha = 1)
ax0.plot(range(10),range(10),'k--')
ax0.axvline(2.9,color = 'm')
ax0.axhline(2.9,color = 'm')
ax0.set_xlim(2.9,6)
ax0.set_ylim(2.9,6)
ax0.set_xlabel('zphot',fontsize = 14)
ax0.set_ylabel('zspec',fontsize = 14)
#ax1.hist(dznw2,bins = 70, histtype = 'step',normed = True,color = '#FFA500',linewidth = 2,label = 'zspec < 2.9')
ax1.hist(dznwhz2, bins = 70, histtype = 'step',normed = False,color = 'r',linewidth = 2,label = 'zspec > 2.9')
ax1.set_xlabel('zphot - zspec',fontsize = 14)
ax1.set_ylabel('Counts',fontsize = 14)
ax1.set_xlim(-0.5,0.5)
fig.tight_layout()
plt.legend()
#plt.savefig('specz_vs_photz_S82.pdf',bbox_inches='tight')
plt.show()
In [20]:
#Split the above histogram in imag
#Less than i = 20.2
fulldiff = zphotnw[(zspec>=2.9) & (imag<=20.2)]-zspec[(zspec>=2.9) & (imag<=20.2)]
fulldiff2 = fulldiff[(fulldiff>0) | (fulldiff <=0)]
#Greater than i=20.2
hzdiffs = zphotnw[(zspec>=2.9) & (imag>=20.2)]-zspec[(zspec>=2.9) & (imag>=20.2)]
hzdiffs2 = hzdiffs[(hzdiffs>0) | (hzdiffs <=0)]
print len(hzdiffs2),len(fulldiff2)
print len(fulldiff2[(fulldiff2>-0.05) & (fulldiff2<0.05)])/float(len(fulldiff2))
print len(hzdiffs2[(hzdiffs2>-0.05) & (hzdiffs2<0.05)])/float(len(hzdiffs2))
bins = np.linspace(-0.5,0.5,50)
#Plotting Parameters (Replace with Group code call!)
params = {'legend.fontsize': 12, 'xtick.labelsize': 12, 'ytick.labelsize': 12, 'xtick.major.width':2, 'xtick.minor.width':2, 'ytick.major.width':2, 'ytick.minor.width':2, 'xtick.major.size':6, 'xtick.minor.size':4, 'ytick.major.size':6, 'ytick.minor.size':4}
plt.rcParams.update(params)
plt.rc("axes", linewidth=2.0)
#Plot for paper
fig = plt.figure(1,figsize = (10,10))
gs = gridspec.GridSpec(1,2, height_ratios=[1, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1])
ax0.scatter(zphotnw[imag>=20.2],zspec[imag>=20.2], s = 2, edgecolor = 'g',facecolor = 'r', alpha = 1,label = None)
ax0.scatter(100,100, s = 20, edgecolor = 'g',facecolor = 'g', label = 'Bright')
ax0.scatter(zphotnw[imag<=20.2],zspec[imag<=20.2], s = 2, edgecolor = '#FFA500',facecolor = '#FFA500', alpha = 1,label = None)
ax0.scatter(100,100, s = 20, edgecolor = '#FFA500',facecolor = '#FFA500', label = 'Faint')
ax0.plot(range(10),range(10),'k--')
ax0.axvline(2.9,color = 'm')
ax0.axhline(2.9,color = 'm')
ax0.set_xlim(2.9,6)
ax0.set_ylim(2.9,6)
ax0.set_xlabel('zphot',fontsize = 14)
ax0.set_ylabel('zspec',fontsize = 14)
ax0.legend(scatterpoints = 1, loc = 2)
ax1.hist(fulldiff2,bins, histtype = 'step',normed = False,color = '#FFA500',linewidth = 2,label = 'Faint')
ax1.hist(hzdiffs2, bins , histtype = 'step',normed = False,color = 'g',linewidth = 2,linestyle = ':',label = 'Bright')
ax1.set_xlabel('zphot - zspec',fontsize = 14)
ax1.set_ylabel('Counts',fontsize = 14)
ax1.set_xlim(-0.2,0.2)
fig.tight_layout()
plt.legend()
plt.savefig('specz_vs_photz_S82.pdf',bbox_inches='tight')
plt.show()
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In [22]:
### Full color-z plots
In [106]:
### DEFINE THE TRAINING SET
#JT PATH ON TRITON to training set after classification
data = Table.read('/Users/johntimlin/Catalogs/QSO_Candidates/Training_set/GTR-ADM-QSO-ir-testhighz_findbw_lup_2016_starclean_with_shenlabel.fits')
#data = Table.read('/home/john/Catalogs/QSO_Candidates/Training_set/GTR-ADM-QSO-ir-testhighz_findbw_lup_2016_starclean_with_shenlabel.fits')
#Fill all cells in arrays
data = data.filled()
# Remove stars from training data
qmask = (data['zspec']>0)
ext = (data['morph']!=6)
pt = (data['morph']==6)
qdata = data[qmask]
edata = data[ext]
pdata = data[pt]
In [107]:
### DEFINE THE TEST SET
# TEST DATA USING 2.9<z<5.4 zrange ON HOME
testdata = Table.read('/Users/johntimlin/Clustering_Paper_Code_Stable/Angular_Clustering_Final/Data_Sets/QSO_Candidates_allcuts_with_errors_visualinsp.fits')
#testdata = Table.read('/home/john/Clustering_Paper_Code_Stable/Angular_Clustering_Final/Data_Sets/QSO_Candidates_allcuts_with_errors_visualinsp.fits')
testdata=testdata.filled()
#Limit to objects that have been classified as quasars
qsocandmask = ((testdata['ypredRFC']==0) | (testdata['ypredSVM']==0) | (testdata['ypredBAG']==0))
testdatacand = testdata[qsocandmask & (testdata['Good_obj'] == 0)]
print len(testdata),len(testdatacand)
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In [108]:
#Find the mean of the distribution
#The purpose of this function is to compute the mean of the color-z distribution using the spectrocopic redshifts of
#known quasars
def Detect_mode(color, z, zbins = np.arange(0,5.5,0.1),sigval=1.0):
modecol = []
sigpl = []
sigmin = []
zavg = []
for i in range(len(zbins)-1):
#separate into the redshift bins
zlow = zbins[i]
zhigh = zbins[i+1]
#Cut the color data according to bin
flatcol = color[((z>=zlow)&(z<=zhigh))]
tempcol = color[((z>=zlow)&(z<=zhigh))][:, np.newaxis] #change the array from row shape (1) to column shape (1,)
#After the KDE is trained on the data colors, input an evenly spaced grid into the score_sample to get density
sample_range = np.linspace(min(tempcol[:, 0]), max(tempcol[:, 0]), len(tempcol[:, 0]))[:, np.newaxis]
#Run the KDE
est = kde(bandwidth=0.1,kernel='epanechnikov') #Set up the Kernel
value = est.fit(tempcol).score_samples(sample_range) #fit the kernel to the data and find the density of the grid
med = sample_range[value == max(value)] #Find where the color with the maximum density
modecol.append(med[0][0]) #append the color to a list for interpolation
## Find the standard deviation of the data
sigma_plus = np.sqrt(np.sum((flatcol[flatcol - med[0][0] >0.0] - med[0][0])**2) / float(len(flatcol[flatcol - med[0][0] >0.0])))
sigma_minus = np.sqrt(np.sum((flatcol[flatcol - med[0][0] <0.0] - med[0][0])**2) / float(len(flatcol[flatcol - med[0][0] <0.0])))
sigpl.append(sigma_plus)
sigmin.append(sigma_minus)
zavg.append((zlow+zhigh)/2.0)
#zavg.append(zlow)
'''
#Plot the KDE for each redshift bin
plt.plot(sample_range[:,0],np.exp(value))
plt.show()
'''
#Interpolate the color redshift relation given the average redshift and the densest colors at each redshift
#Also interpolates the 1 sigma line above and below the mean
col = interpolate.interp1d(zavg,np.asarray(modecol))
sp = interpolate.interp1d(zavg,np.asarray(modecol)+sigval*np.asarray(sigpl))
sm = interpolate.interp1d(zavg,np.asarray(modecol)-sigval*np.asarray(sigmin))
return col,sp,sm
In [109]:
ugmode = Detect_mode(qdata['ug'],qdata['zspec'])
grmode = Detect_mode(qdata['gr'],qdata['zspec'])
rimode = Detect_mode(qdata['ri'],qdata['zspec'])
izmode = Detect_mode(qdata['iz'],qdata['zspec'])
zc1mode = Detect_mode(qdata['zs1'],qdata['zspec'])
c1c2mode = Detect_mode(qdata['s1s2'],qdata['zspec'])
In [110]:
modeug = ugmode[0](np.linspace(0.1,5.3,100))
modegr = grmode[0](np.linspace(0.1,5.3,100))
moderi = rimode[0](np.linspace(0.1,5.3,100))
modeiz = izmode[0](np.linspace(0.1,5.3,100))
modezc1 = zc1mode[0](np.linspace(0.1,5.3,100))
modec1c2 = c1c2mode[0](np.linspace(0.1,5.3,100))
In [111]:
#Plot for paper
fig = plt.figure(2,figsize = (10,10))
gs = gridspec.GridSpec(6, 1)#, width_ratios=[1,1])#,1,1,1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1],sharex = ax0)
ax2 = plt.subplot(gs[2],sharex = ax1)
ax3 = plt.subplot(gs[3],sharex = ax2)
ax4 = plt.subplot(gs[4],sharex = ax3)
ax5 = plt.subplot(gs[5],sharex = ax4)
plt.axes(ax0)
hex_contour(qdata['zspec'],qdata['ug'], levels=[0.5,0.99], std=True, min_cnt=7, smoothing=4, hkwargs={'gridsize':75}, skwargs={'color':'k','alpha':0.5,'marker':'.'}, ckwargs={'colors':'k','alpha':1,'linewidths':1})
plt.plot(np.linspace(0.1,5.3,100),modeug, linewidth = 2,linestyle = '--', color = '#33B8FF')
plt.scatter(testdatacand['zphotNW'],testdatacand['ug'],s = 1, color = '#fd8d3c',alpha = 0.5, zorder = 100)
ax0.set_xlim(0,6)
ax0.yaxis.set_ticks([0,2,4,6])
ax0.set_ylabel('ug',fontsize = 14)
plt.axes(ax1)
hex_contour(qdata['zspec'],qdata['gr'], levels=[0.5,0.99], std=True, min_cnt=7, smoothing=4, hkwargs={'gridsize':75}, skwargs={'color':'k','alpha':0.5,'marker':'.'}, ckwargs={'colors':'k','alpha':1,'linewidths':1})
plt.plot(np.linspace(0.1,5.3,100),modegr, linewidth = 2,linestyle = '--', color = '#33B8FF')
plt.scatter(testdatacand['zphotNW'],testdatacand['gr'],s = 1, color = '#fd8d3c',alpha = 0.255, zorder = 100)
#ax1.set_xlim(0,6)
ax1.yaxis.set_ticks([0,2,4])
ax1.set_ylabel('gr',fontsize = 14)
plt.axes(ax2)
hex_contour(qdata['zspec'],qdata['ri'], levels=[0.5,0.99], std=True, min_cnt=7, smoothing=4, hkwargs={'gridsize':75}, skwargs={'color':'k','alpha':0.5,'marker':'.'}, ckwargs={'colors':'k','alpha':1,'linewidths':1})
plt.plot(np.linspace(0.1,5.3,100),moderi, linewidth = 2,linestyle = '--', color = '#33B8FF')
plt.scatter(testdatacand['zphotNW'],testdatacand['ri'],s = 1, color = '#fd8d3c',alpha = 0.5, zorder = 100)
#ax2.set_xlim(0,6)
ax2.yaxis.set_ticks([0,1,2])
ax2.set_ylabel('ri',fontsize = 14)
plt.axes(ax3)
hex_contour(qdata['zspec'],qdata['iz'], levels=[0.5,0.99], std=True, min_cnt=7, smoothing=4, hkwargs={'gridsize':75}, skwargs={'color':'k','alpha':0.5,'marker':'.'}, ckwargs={'colors':'k','alpha':1,'linewidths':1})
plt.plot(np.linspace(0.1,5.3,100),modeiz, linewidth = 2,linestyle = '--', color = '#33B8FF')
plt.scatter(testdatacand['zphotNW'],testdatacand['iz'],s = 1, color = '#fd8d3c',alpha = 0.5, zorder = 100)
#ax3.set_xlim(0,6)
ax3.yaxis.set_ticks([-1,0,1,2])
ax3.set_ylabel('iz',fontsize = 14)
plt.axes(ax4)
hex_contour(qdata['zspec'],qdata['zs1'], levels=[0.5,0.99], std=True, min_cnt=7, smoothing=4, hkwargs={'gridsize':75}, skwargs={'color':'k','alpha':0.5,'marker':'.'}, ckwargs={'colors':'k','alpha':1,'linewidths':1})
plt.plot(np.linspace(0.1,5.3,100),modezc1, linewidth = 2,linestyle = '--', color = '#33B8FF')
plt.scatter(testdatacand['zphotNW'],testdatacand['zs1'],s = 1, color = '#fd8d3c',alpha = 0.5, zorder = 100)
#ax4.set_xlim(0,6)
ax4.yaxis.set_ticks([-1,0,1,2,3])
ax4.set_ylabel('zch1',fontsize = 14)
plt.axes(ax5)
hex_contour(qdata['zspec'],qdata['s1s2'], levels=[0.5,0.99], std=True, min_cnt=7, smoothing=4, hkwargs={'gridsize':75}, skwargs={'color':'k','alpha':0.5,'marker':'.'}, ckwargs={'colors':'k','alpha':1,'linewidths':1})
plt.plot(np.linspace(0.1,5.3,100),modec1c2, linewidth = 2,linestyle = '--', color = '#33B8FF')
plt.scatter(testdatacand['zphotNW'],testdatacand['s1s2'],s = 1, color = '#fd8d3c',alpha = 0.5, zorder = 100)
#ax5.set_xlim(0,6)
ax5.set_ylabel('ch1ch2',fontsize = 14)
ax5.yaxis.set_ticks([0,1])
plt.subplots_adjust(hspace=.0)
plt.setp(ax0.get_xticklabels(), visible=False)
plt.setp(ax1.get_xticklabels(), visible=False)
plt.setp(ax2.get_xticklabels(), visible=False)
plt.setp(ax3.get_xticklabels(), visible=False)
plt.setp(ax4.get_xticklabels(), visible=False)
#fig.tight_layout()
plt.xlabel('Redshift',fontsize = 14)
#plt.savefig('col_specz.pdf',bbox_inches='tight')
plt.show()
In [22]:
#Single color plot
fig = plt.figure(3,figsize = (8,8))
hex_contour(qdata['zspec'],qdata['zs1'], levels=[0.25,0.5,0.75,0.99], std=True, min_cnt=7, smoothing=4, hkwargs={'gridsize':75}, skwargs={'color':'k','alpha':0.9,'marker':'.'}, ckwargs={'colors':'k','alpha':1,'linewidths':1})
plt.scatter(testdatacand['zphotNW'],testdatacand['zs1'],s = 1, color = '#fd8d3c',alpha = 1, zorder = 100)
plt.plot(np.linspace(0.1,5.3,100),modezc1, linewidth = 2,linestyle = '--', color = '#33B8FF',zorder = 200)
know = plt.scatter(100,100,color = 'k')
cand = plt.scatter(100,100,color = '#fd8d3c')
plt.xlim(0,6)
plt.ylim(-2,4.2)
#plt.yaxis.set_ticks([-1,0,1,2,3])
plt.ylabel('zch1',fontsize = 14)
plt.xlabel('Redshift',fontsize = 14)
first_legend = plt.legend([know,cand],['Known QSOs','Candidates'], loc=1,scatterpoints = 1)
plt.gca().add_artist(first_legend)
plt.savefig('col_specz_single.pdf',bbox_inches='tight')
plt.show()
In [113]:
#Single color plot
fig = plt.figure(3,figsize = (8,8))
hex_contour(qdata['zspec'],qdata['ri'], levels=[0.5,0.99], std=True, min_cnt=7, smoothing=4, hkwargs={'gridsize':75}, skwargs={'color':'k','alpha':0.9,'marker':'.'}, ckwargs={'colors':'k','alpha':1,'linewidths':1})
plt.scatter(testdatacand['zphotNW'],testdatacand['ri'],s = 1, color = '#fd8d3c',alpha = 0.5, zorder = 100)
plt.plot(np.linspace(0.1,5.3,100),moderi, linewidth = 2,linestyle = '--', color = '#33B8FF')
know = plt.scatter(100,100,color = 'k')
cand = plt.scatter(100,100,color = '#fd8d3c')
plt.xlim(0,6)
plt.ylim(-1.5,2.5)
#plt.yaxis.set_ticks([-1,0,1,2,3])
plt.ylabel('zch1',fontsize = 14)
plt.xlabel('Redshift',fontsize = 14)
first_legend = plt.legend([know,cand],['Known QSOs','Candidates'], loc=1,scatterpoints = 1)
plt.gca().add_artist(first_legend)
#plt.savefig('col_specz_single.pdf',bbox_inches='tight')
plt.show()
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In [114]:
#Single color plot
lowz = qdata['zspec']<=2.25
midz = (qdata['zspec']>2.25)&(qdata['zspec']<=3.45)
highz= qdata['zspec']>=2.9
fig = plt.figure(9,figsize = (8,8))
hex_contour(edata['ri'],edata['iz'], levels=[0.1,0.3,0.4,0.5,0.6,0.7,0.9,0.99], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#A9A9A9','alpha':0.5,'marker':'.'}, ckwargs={'colors':'#A9A9A9','alpha':1,'linewidths':1})
hex_contour(pdata['ri'],pdata['iz'], levels=[0.5,0.7,0.99], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#4eb3d3','alpha':0.5,'marker':'.'}, ckwargs={'colors':'#4eb3d3','alpha':1,'linewidths':1})
#hex_contour(qdata['ri'][lowz],qdata['iz'][lowz], levels=[0.1,0.3,0.4,0.5,0.6,0.7,0.9,0.99], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'b','alpha':0.5,'marker':'.'}, ckwargs={'colors':'b','alpha':1,'linewidths':1})
#hex_contour(qdata['ri'][midz],qdata['iz'][midz], levels=[0.1,0.3,0.4,0.5,0.6,0.77,0.9,0.99], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'c','alpha':0.5,'marker':'.'}, ckwargs={'colors':'c','alpha':1,'linewidths':1})
hex_contour(qdata['ri'][highz],qdata['iz'][highz], levels=[0.5,0.6,0.7,0.9], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#08589e','alpha':1,'marker':'.'}, ckwargs={'colors':'#08589e','alpha':1,'linewidths':1})
hex_contour(testdatacand['ri'],testdatacand['iz'], levels=[0.1,0.2,0.3,0.4,0.5,0.6,0.7], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#fd8d3c','alpha':1,'marker':'.'}, ckwargs={'colors':'#fd8d3c','alpha':1,'linewidths':1})
#plt.scatter(testdatacand['ri'],testdatacand['iz'],s = 1, color = '#fd8d3c',alpha = 1, zorder = 100)
#know = plt.scatter(100,100,color = 'k')
#cand = plt.scatter(100,100,color = '#fd8d3c')
plt.xlim(-1.3,3)
plt.ylim(-2,3)
#plt.yaxis.set_ticks([-1,0,1,2,3])
plt.ylabel('ri',fontsize = 14)
plt.xlabel('iz',fontsize = 14)
#first_legend = plt.legend([know,cand],['Known QSOs','Candidates'], loc=1,scatterpoints = 1)
#plt.gca().add_artist(first_legend)
#plt.savefig('col_specz_single.pdf',bbox_inches='tight')
plt.show()
In [146]:
#Color-color Plot for paper
lowz = qdata['zspec']<=2.25
midz = (qdata['zspec']>2.25)&(qdata['zspec']<=3.45)
highz= qdata['zspec']>=2.9
fig = plt.figure(12,figsize = (16,16))
gs = gridspec.GridSpec(1, 3, height_ratios=[1,1,1,1,1],width_ratios=[1,1,1,1,1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1])
ax2 = plt.subplot(gs[2])
plt.axes(ax0)
hex_contour(edata['ug'],edata['gr'], levels=[0.1,0.3,0.4,0.5,0.6,0.7,0.9,0.99], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#A9A9A9','alpha':0.5,'marker':''}, ckwargs={'colors':'#A9A9A9','alpha':1,'linewidths':1})
hex_contour(pdata['ug'],pdata['gr'], levels=[0.3,0.5,0.7,0.99], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#4eb3d3','alpha':0.5,'marker':''}, ckwargs={'colors':'#4eb3d3','alpha':1,'linewidths':1})
hex_contour(qdata['ug'][highz],qdata['gr'][highz], levels=[0.3,0.5,0.6,0.7,0.9], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':25}, skwargs={'color':'#08589e','alpha':1,'marker':','}, ckwargs={'colors':'#08589e','alpha':1,'linewidths':1})
hex_contour(testdatacand['ug'],testdatacand['gr'], levels=[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':25}, skwargs={'color':'#fd8d3c','alpha':1,'marker':','}, ckwargs={'colors':'#fd8d3c','alpha':1,'linewidths':1})
ax0.set_xlim(-2,6)
ax0.set_ylim(-1,4)
ax0.set_xlabel('ug',fontsize = 14)
ax0.set_ylabel('gr',fontsize = 14)
ax0.minorticks_on()
plt.axes(ax1)
hex_contour(edata['gr'],edata['ri'], levels=[0.1,0.3,0.4,0.5,0.6,0.7,0.9,0.99], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#A9A9A9','alpha':0.5,'marker':''}, ckwargs={'colors':'#A9A9A9','alpha':1,'linewidths':1})
hex_contour(pdata['gr'],pdata['ri'], levels=[0.3,0.5,0.7,0.99], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#4eb3d3','alpha':0.5,'marker':''}, ckwargs={'colors':'#4eb3d3','alpha':1,'linewidths':1})
hex_contour(qdata['gr'][highz],qdata['ri'][highz], levels=[0.3,0.5,0.6,0.7,0.9], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#08589e','alpha':1,'marker':','}, ckwargs={'colors':'#08589e','alpha':1,'linewidths':1})
hex_contour(testdatacand['gr'],testdatacand['ri'], levels=[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':25}, skwargs={'color':'#fd8d3c','alpha':1,'marker':','}, ckwargs={'colors':'#fd8d3c','alpha':1,'linewidths':1})
ax1.set_xlim(-1,4)
ax1.set_ylim(-1,3)
ax1.set_xlabel('gr',fontsize = 14)
ax1.set_ylabel('ri',fontsize = 14)
ax1.minorticks_on()
plt.axes(ax2)
hex_contour(edata['ri'],edata['iz'], levels=[0.1,0.3,0.4,0.5,0.6,0.7,0.9,0.99], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#A9A9A9','alpha':0.5,'marker':''}, ckwargs={'colors':'#A9A9A9','alpha':1,'linewidths':1})
hex_contour(pdata['ri'],pdata['iz'], levels=[0.3,0.5,0.7,0.99], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#4eb3d3','alpha':0.5,'marker':''}, ckwargs={'colors':'#4eb3d3','alpha':1,'linewidths':1})
hex_contour(qdata['ri'][highz],qdata['iz'][highz], levels=[0.3,0.5,0.6,0.7,0.9], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#08589e','alpha':1,'marker':','}, ckwargs={'colors':'#08589e','alpha':1,'linewidths':1})
hex_contour(testdatacand['ri'],testdatacand['iz'], levels=[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':25}, skwargs={'color':'#fd8d3c','alpha':1,'marker':','}, ckwargs={'colors':'#fd8d3c','alpha':1,'linewidths':1})
ax2.set_xlim(-1,3)
ax2.set_ylim(-1.5,1.5)
ax2.set_xlabel('ri',fontsize = 14)
ax2.set_ylabel('iz',fontsize = 14)
ax2.xaxis.set_ticks([-1,0,1,2,3])
ax2.minorticks_on()
fig.tight_layout()
plt.savefig('opt_cand_colors.pdf',bbox_inches='tight')
plt.show()
In [147]:
#Plot the infrared
highz= qdata['zspec']>=2.9
fig = plt.figure(13,figsize = (16,16))
gs = gridspec.GridSpec(1, 2, height_ratios=[1,1,1,1,1],width_ratios=[1,1,1,1,1])
ax3 = plt.subplot(gs[0])
ax4 = plt.subplot(gs[1])
plt.axes(ax3)
hex_contour(edata['iz'],edata['zs1'], levels=[0.1,0.3,0.4,0.5,0.6,0.7,0.9,0.99], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#A9A9A9','alpha':0.5,'marker':''}, ckwargs={'colors':'#A9A9A9','alpha':1,'linewidths':1})
hex_contour(pdata['iz'],pdata['zs1'], levels=[0.3,0.5,0.7,0.99], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#4eb3d3','alpha':0.5,'marker':''}, ckwargs={'colors':'#4eb3d3','alpha':1,'linewidths':1})
hex_contour(qdata['iz'][highz],qdata['zs1'][highz], levels=[0.3,0.5,0.6,0.7,0.9], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#08589e','alpha':1,'marker':','}, ckwargs={'colors':'#08589e','alpha':1,'linewidths':1})
hex_contour(testdatacand['iz'],testdatacand['zs1'], levels=[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':25}, skwargs={'color':'#fd8d3c','alpha':1,'marker':','}, ckwargs={'colors':'#fd8d3c','alpha':1,'linewidths':1})
ax3.set_xlim(-1,2)
ax3.set_ylim(-3,4)
ax3.set_xlabel('iz',fontsize = 14)
ax3.set_ylabel('zch1',fontsize = 14)
ax3.minorticks_on()
plt.axes(ax4)
hex_contour(edata['zs1'],edata['s1s2'], levels=[0.1,0.3,0.4,0.5,0.6,0.7,0.9,0.99], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#A9A9A9','alpha':0.5,'marker':''}, ckwargs={'colors':'#A9A9A9','alpha':1,'linewidths':1})
hex_contour(pdata['zs1'],pdata['s1s2'], levels=[0.3,0.5,0.7,0.99], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':50}, skwargs={'color':'#4eb3d3','alpha':0.5,'marker':''}, ckwargs={'colors':'#4eb3d3','alpha':1,'linewidths':1})
hex_contour(qdata['zs1'][highz],qdata['s1s2'][highz], levels=[0.3,0.5,0.6,0.7,0.9], std=True, min_cnt=5, smoothing=1, hkwargs={'gridsize':25}, skwargs={'color':'#08589e','alpha':1,'marker':','}, ckwargs={'colors':'#08589e','alpha':1,'linewidths':1})
hex_contour(testdatacand['zs1'],testdatacand['s1s2'], levels=[0.1,0.3,0.4,0.5,0.6,0.7,0.8,0.9], std=True, min_cnt=5, smoothing=2, hkwargs={'gridsize':25}, skwargs={'color':'#fd8d3c','alpha':1,'marker':','}, ckwargs={'colors':'#fd8d3c','alpha':1,'linewidths':1})
ax4.set_xlim(-2,4)
ax4.set_ylim(-1,1)
ax4.set_xlabel('zch1',fontsize = 14)
ax4.set_ylabel('ch1ch2',fontsize = 14)
ax4.minorticks_on()
fig.tight_layout()
plt.savefig('ir_cand_colors.pdf',bbox_inches='tight')
plt.show()
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